12,287 research outputs found
Dimension Reduction by Mutual Information Discriminant Analysis
In the past few decades, researchers have proposed many discriminant analysis
(DA) algorithms for the study of high-dimensional data in a variety of
problems. Most DA algorithms for feature extraction are based on
transformations that simultaneously maximize the between-class scatter and
minimize the withinclass scatter matrices. This paper presents a novel DA
algorithm for feature extraction using mutual information (MI). However, it is
not always easy to obtain an accurate estimation for high-dimensional MI. In
this paper, we propose an efficient method for feature extraction that is based
on one-dimensional MI estimations. We will refer to this algorithm as mutual
information discriminant analysis (MIDA). The performance of this proposed
method was evaluated using UCI databases. The results indicate that MIDA
provides robust performance over different data sets with different
characteristics and that MIDA always performs better than, or at least
comparable to, the best performing algorithms.Comment: 13pages, 3 tables, International Journal of Artificial Intelligence &
Application
Advances in Feature Selection with Mutual Information
The selection of features that are relevant for a prediction or
classification problem is an important problem in many domains involving
high-dimensional data. Selecting features helps fighting the curse of
dimensionality, improving the performances of prediction or classification
methods, and interpreting the application. In a nonlinear context, the mutual
information is widely used as relevance criterion for features and sets of
features. Nevertheless, it suffers from at least three major limitations:
mutual information estimators depend on smoothing parameters, there is no
theoretically justified stopping criterion in the feature selection greedy
procedure, and the estimation itself suffers from the curse of dimensionality.
This chapter shows how to deal with these problems. The two first ones are
addressed by using resampling techniques that provide a statistical basis to
select the estimator parameters and to stop the search procedure. The third one
is addressed by modifying the mutual information criterion into a measure of
how features are complementary (and not only informative) for the problem at
hand
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